Lecturer at American International University, Bangladesh
Author Profile
Summary
Shakila Rahman is a dedicated academician currently serving as a Lecturer in the Department of Computer Science at the Faculty of Science and Technology, American International University-Bangladesh (AIUB). She holds a strong academic background in Artificial Intelligence and Computer Engineering, with her research focusing on emerging areas such as UAV networking, wireless sensor networks, optimization algorithms, and machine learning. Shakila is actively involved in mentoring students, guiding projects, and publishing impactful research in reputed platforms.
Educational Details
Shakila Rahman earned her M.Sc. in AI & Computer Engineering from the University of Ulsan, South Korea, in 2023 with an impressive CGPA of 4.00 out of 4.50. She completed her B.Sc. in Computer Science and Engineering from International Islamic University Chittagong (IIUC), Bangladesh, in 2019, securing a CGPA of 3.743 out of 4.00. Prior to her university education, she completed her Higher Secondary Certificate (HSC) from Cox’s Bazar Govt. College and Secondary School Certificate (SSC) from Cox’s Bazar Govt. Girls’ High School.
Professional Experience
Shakila is currently employed as a Lecturer in the Department of Computer Science and Engineering at AIUB, Dhaka, Bangladesh, where she has been working since January 2023. She previously served as a Graduate Research Assistant at the University of Ulsan, South Korea, from September 2020 to December 2022 under Professor Seokhoon Yoon. Additionally, she worked as an Undergraduate Teaching Assistant at IIUC in 2019. She has participated in technical boot camps and workshops and actively contributes to academic supervision, having guided several student projects and a machine learning-based thesis group.
Research Interests
Her research interests span a wide range of cutting-edge topics including UAV Networking, Wireless Sensor Networks, Network Systems, Optimization Algorithms, Machine Learning, Deep Learning, Image Processing, and AR/VR Applications in Artificial Intelligence. These multidisciplinary areas reflect her focus on building intelligent and adaptive systems for real-world applications.
Author Metrics
Shakila Rahman actively maintains a presence on prominent academic platforms. Her ResearchGate profile can be found at https://www.researchgate.net/profile/Shakila-Rahman-3, and her ORCID ID is 0000-0001-6375-4174. She is also available on LinkedIn at Shakila Rahman. Her published works and citation records are regularly updated on these platforms.
Awards and Honors
During her master's studies, Shakila was awarded the prestigious Brain Korea 21 (BK21) Scholarship and a fully funded AF1 scholarship at the University of Ulsan, valued at approximately USD 21,000. She also received funding from Korean Government-supported National Research Foundation (NRF) projects to support her graduate research publications. These accolades recognize her academic excellence and research contributions in the field of computer science and engineering.
Publication Top Noted
1. Bilingual Sign Language Recognition: A YOLOv11-Based Model for Bangla and English Alphabets
Authors: N. Navin, F.A. Farid, R.Z. Rakin, S.S. Tanzim, M. Rahman, S. Rahman, J. Uddin, ...
Journal: Journal of Imaging, Vol. 11, Issue 5, Article 134
Year: 2025
Citation: 1 (as of now)
Summary:
This study introduces a YOLOv11-based deep learning model designed to recognize both Bangla and English sign language alphabets in real-time. The model was trained on a custom bilingual sign dataset and achieved high accuracy and low latency. The contribution is notable in promoting inclusivity for hearing-impaired communities in multilingual regions like Bangladesh.
2. Towards Safer Cities: AI-Powered Infrastructure Fault Detection Based on YOLOv11
Authors: R.Z. Rakin, M. Rahman, K.F. Borsa, F.A. Farid, S. Rahman, J. Uddin, H.A. Karim
Journal: Future Internet, Vol. 17, Issue 5, Article 187
Year: 2025
Summary:
This paper proposes an AI model using YOLOv11 to identify infrastructure faults (e.g., road cracks, bridge damage) through image data. Designed with smart city integration in mind, the model is tested in urban environments and demonstrates high efficiency.
3. A Hybrid CNN Framework DLI-Net for Acne Detection with XAI
Authors: S. Sharmin, F.A. Farid, M. Jihad, S. Rahman, J. Uddin, R.K. Rafi, R. Hossan, ...
Journal: Journal of Imaging, Vol. 11, Issue 4, Article 115
Year: 2025
Summary:
This paper presents DLI-Net, a hybrid CNN framework for classifying and explaining acne severity. It incorporates Explainable AI (XAI) techniques to enhance trust and transparency in medical AI systems.
4. A Deep Q-Learning Based UAV Detouring Algorithm in a Constrained Wireless Sensor Network Environment
Authors: S. Rahman, S. Akter, S. Yoon
Journal: Electronics, Vol. 14, Issue 1, Article 1
Year: 2024
Citation: 2 (as of now)
Summary:
This study explores a reinforcement learning-based approach using Deep Q-Learning for UAV navigation in constrained wireless sensor networks. The algorithm optimizes path planning in real-time, even in environments with signal interference or node failures.
5. A Deep Learning Model for YOLOv9-based Human Abnormal Activity Detection: Violence and Non-Violence Classification
Authors: S. Salehin, S. Rahman, M. Nur, A. Asif, M. Bin Harun, J. Uddin
Journal: Iranian Journal of Electrical & Electronic Engineering, Vol. 20, Issue 4
Year: 2024
Citation: 2 (as of now)
Summary:
This paper proposes a YOLOv9-based model to detect abnormal human activity, particularly violent behavior, in real-time video surveillance. The system is trained on public datasets and achieves high detection accuracy.
Conclusion
Ms. Shakila Rahman is a promising and emerging researcher, with an impressive blend of academic excellence, funded research, and contributions to cutting-edge domains like machine learning and UAV networks. Her commitment to mentoring students and publishing research makes her a very strong candidate for the Best Researcher Award, particularly among early-career researchers or those in developing countries.